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Computer Vision (elective)

Full course description

Can we make machines look, understand and interpret the world around them? Can we make cars that can autonomously navigate in the world, robots that can recognize and grasp objects and, ultimately, recognize humans and communicate with them? How do search engines index and retrieve billions of images? This course will provide the knowledge and skills that are fundamental to core vision tasks of one of the fastest growing fields in academia and industry: visual computing. Topics include introduction to fundamental problems of computer vision, mathematical models and computational methodologies for their solution, implementation of real-life applications and experimentation with various techniques in the field of scene analysis and understanding. In particular, after a recap of basic image analysis tools (enhancement, restoration, color spaces, edge detection), students will learn about the most powerful and widely used feature detectors (SIFT, Harris) and trackers (optical flow), fitting, image geometric transformation and mosaicing techniques, texture analysis and classification using unsupervised techniques, object classification and face recognition, camera models, epipolar geometry and 3D reconstruction from 2D views



Digital Image Processing”, Rafael C. Gonzalez & Richard E. Woods, Addison-Wesley, 2002 / Digital Image Processing using MATLAB. 2° Edition Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins. Gatesmark Publishing


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study/master_ai/year_1/block_5/computer_vision.txt · Last modified: 2021/07/04 14:21 by nicolasperez